Advanced non-linear control based on artificial intelligence tuner for hand tremor suppression

Abstract

One of the most common symptoms among diverse patients (e.g. Parkinson’s disease, essential tremor) is hand tremor. Deep brain stimulation (DBS) has been a popular method of controlling hand tremor in recent years. Although recent advancements in the DBS system have enabled DBS applications in the context of various neurodegenerative disorders, the deployment of such interface tactics in DBS applications is seriously threatened by un-adaptability concerns and a lack of an accurate model. In response to these issues, the current letter discusses the construction of an intelligent technique for minimizing hand tremor and self-adaptive DBS settings without requiring a proper hand tremor system modeling setup. For this purpose, the hand tremor is suppressed using an intelligent proportional–integral (iPI) as the main controller and the sliding mode (SM) as an observer. Furthermore, as an auxiliary controller, a deep reinforcement learning (DRL) method with the Actor-Critic structure is adaptively incorporated. The feedback parameters of the ultra-local model (ULM) controller will be adaptively modified utilizing the DRL method by online neural network (NN) updating in the proposed strategy. The advantages of the ULM based on DRL structure over state-of-the-art approaches are demonstrated by a complete assessment.